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A Markovian approach for modelling the effects of maintenance on downtime and failure risk of wind turbine components

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  • Ossai, Chinedu I.
  • Boswell, Brian
  • Davies, Ian J.

Abstract

For effective and efficient performance of wind turbines, components and systems should perform at a low risk with minimal downtime. To establish the impacts of wind turbine components maintenance on downtime and failure risks, a six state Markov model was developed using the failure rates and downtimes information. The transition and maintenance rates at the lifecycle phases (introduction, maturity, ageing and terminal) together with those at maintenance and failure phases were determined using a calibrated survivability index whilst the transition rate probabilities were used in modelling the performance and failure risks probabilities at different maintenance intervals. The model was tested using failure rates and downtime information of wind turbine components obtained from literature and the results indicates that the model has practical applications for managing wind turbines.

Suggested Citation

  • Ossai, Chinedu I. & Boswell, Brian & Davies, Ian J., 2016. "A Markovian approach for modelling the effects of maintenance on downtime and failure risk of wind turbine components," Renewable Energy, Elsevier, vol. 96(PA), pages 775-783.
    • Handle: RePEc:eee:renene:v:96:y:2016:i:pa:p:775-783
    DOI: 10.1016/j.renene.2016.05.022
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    References listed on IDEAS

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    Cited by:

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    2. Thomas Michael Welte & Iver Bakken Sperstad & Espen Høegh Sørum & Magne Lorentzen Kolstad, 2017. "Integration of Degradation Processes in a Strategic Offshore Wind Farm O&M Simulation Model," Energies, MDPI, vol. 10(7), pages 1-18, July.
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    5. Kathleen Mallard & Vincent Debusschere & Lauric Garbuio, 2020. "Multi-Criteria Method for Sustainable Design of Energy Conversion Systems," Sustainability, MDPI, vol. 12(16), pages 1-18, August.
    6. Kang, Jichuan & Sun, Liping & Guedes Soares, C., 2019. "Fault Tree Analysis of floating offshore wind turbines," Renewable Energy, Elsevier, vol. 133(C), pages 1455-1467.
    7. Haileyesus B. Endeshaw & Stephen Ekwaro-Osire & Fisseha M. Alemayehu & João Paulo Dias, 2017. "Evaluation of Fatigue Crack Propagation of Gears Considering Uncertainties in Loading and Material Properties," Sustainability, MDPI, vol. 9(12), pages 1-15, November.
    8. Shafiee, Mahmood & Sørensen, John Dalsgaard, 2019. "Maintenance optimization and inspection planning of wind energy assets: Models, methods and strategies," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    9. Masoud Asgarpour & John Dalsgaard Sørensen, 2018. "Bayesian Based Diagnostic Model for Condition Based Maintenance of Offshore Wind Farms," Energies, MDPI, vol. 11(2), pages 1-17, January.
    10. Yuri Merizalde & Luis Hernández-Callejo & Oscar Duque-Perez & Víctor Alonso-Gómez, 2019. "Maintenance Models Applied to Wind Turbines. A Comprehensive Overview," Energies, MDPI, vol. 12(2), pages 1-41, January.
    11. Arzaghi, Ehsan & Abaei, Mohammad Mahdi & Abbassi, Rouzbeh & O'Reilly, Malgorzata & Garaniya, Vikram & Penesis, Irene, 2020. "A Markovian approach to power generation capacity assessment of floating wave energy converters," Renewable Energy, Elsevier, vol. 146(C), pages 2736-2743.

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